Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Information Technology
Aida Fitriyani
Universitas Bhayangkara Jakarta Raya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Forecasting Inventory Demand Under Volatile Sales Patterns Using the Prophet Algorithm Rafika Sari; Ratna Salkiawati; Nur`aini Puji Lestari; Aida Fitriyani
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2032

Abstract

Inventory availability is a critical factor for companies to maintain operational continuity and customer satisfaction. However, many organizations still face challenges in forecasting demand, particularly when sales patterns are highly volatile and irregular. Although the Prophet forecasting algorithm has been widely used for time-series prediction, its behavior and robustness under unstable sales patterns remain insufficiently examined in practical inventory contexts. This study aims to evaluate the ability of the Prophet algorithm to forecast inventory demand using historical sales data characterized by fluctuating patterns. A quantitative time-series forecasting approach was applied using one year of secondary sales data obtained from PT XYZ. The data were cleaned to address missing values and aggregated into weekly time intervals to reduce noise. Five products with the highest transaction frequency were selected as case studies. Forecasting performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that Prophet is capable of generating reasonably accurate forecasts even under volatile demand conditions. The evaluation results indicate RMSE values ranging from 5.41 to 52.78 and MAPE values ranging from 5% to 23.46% across the five analyzed products. These findings provide empirical evidence that the Prophet algorithm can maintain forecasting robustness despite irregular demand patterns. However, the absence of comparisons with alternative forecasting models limits the strength of conclusions regarding its relative performance. This study contributes by providing empirical insight into the application of Prophet for inventory forecasting under volatile sales conditions and offers practical implications for improving inventory planning in data-driven decision-making environments.
Implementation of the Collaborative Filtering Algorithm in a Cultural Event Recommendation Application for Bekasi City Nurfiyah; Aida Fitriyani; Mohammad Rizqi Rias Firmansyah; Ratna Salkiawati; Hendarman Lubis
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 3 (2026): Volume 7 Number 3 September 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v7i3.2620

Abstract

The dissemination of information regarding cultural events in Bekasi City has not yet been fully optimized, resulting in uneven public participation across various cultural activities. This issue arises from the absence of a recommendation system capable of suggesting cultural events based on users' interests and preferences. This study aims to implement the User-Based Collaborative Filtering algorithm with the Pearson Correlation approach in a web-based cultural event recommendation system. The research methodology involved observation, interviews, and the collection of user rating data from 100 respondents, with the sample size determined using the Slovin formula. The recommendation process was carried out by calculating the average rating of each user, measuring the similarity between users using the Pearson Correlation coefficient, and predicting ratings for events that had not yet been evaluated by the target user. The results demonstrate that the proposed system is capable of identifying similarities in user preferences and generating personalized event recommendations that align with users' interests. In the case study, the predicted rating for User1 on the Pesta Nada event was 3.76, indicating a high likelihood that the event would be of interest to the user and therefore suitable for recommendation. The implementation of this recommendation system is expected to assist the public in discovering cultural events that match their preferences while supporting the Bekasi City Tourism and Culture Office in disseminating event information more effectively, thereby increasing public participation in cultural activities.
Co-Authors Hendarman Lubis Mohammad Rizqi Rias Firmansyah Nurfiyah Nur`aini Puji Lestari RAFIKA SARI Ratna Salkiawati